syllabus‎ > ‎R: Intro‎ > ‎

Helet Botha (1)

4 October, Introductory Chapter
Comments

Q: How does one determine cognitive load (empirically)?
A: Paas and van Merriënboer[16] developed a construct (known as relative condition efficiency) which helps researchers measure perceived mental effort, an index of cognitive load. This construct provides a relatively simple means of comparing instructional conditions. It combines mental effort ratings with performance scores. Group mean z-scores are graphed and may be compared with a one-way Analysis of variance (ANOVA).
Paas and van Merriënboer used relative condition efficiency to compare three instructional conditions (worked examples, completion problems, and discovery practice). They found learners who studied worked examples were the most efficient, followed by those who used the problem completion strategy. Since this early study many other researchers have used this and other constructs to measure cognitive load as it relates to learning and instruction.[17]
The ergonomic approach seeks a quantitative neurophysiological expression of cognitive load which can be measured using common instruments, for example using the heart rate-blood pressure product (RPP) as a measure of both cognitive and physical occupational workload.[18] They believe that it may be possible to use RPP measures to set limits on workloads and for establishing work allowance.
(http://en.wikipedia.org/wiki/Cognitive_load#Measurement_of_cognitive_load)
 
William Playfair is credited for 'inventing' tables, pie charts...



Comment for discussion:
"In contrast, non-spatial data can be visually en-coded using spatial position, but that encoding is chosen by the designer rather than given implicitly in the semantics of the dataset itself. This choice is the one of the most central and difficult problems of visualization design. Which factors will play a role in solving this problem (eg. choosing the amount of and which particual dimensions to consider)? I'm considering whether the domain problem might 'frame' the data in such a way that the visual representation turns out to be less insightful than it could have been... the bottom line might be: even though 'deriving dimensions' might be powerful, it needs to be considered thorougly.

"The next layer is used to determine whether the abstraction from the domain problem into operations on specific data types actually solves the desired problem. After a prototype or finished tool has been deployed, a field study can be carried out to observe whether and how it is used by its intended audience. Also, images produced by the system can be analyzed both qualitatively and quantitatively." (measurement for securing the 'right'/most appropriate derivations).
 
Example of visual popout (in practice)




Oclusion is indeed a problem when using certain 'views' in Eidos Parmenides/ think tools.

"Another problem with 3D is perspective distortion. Although real-world objects do indeed appear smaller when they are further from our eyes, foreshortening makes direct comparison of object heights difficult (Tory et al., 2006). Once again, although we can often judge the heights of familiar objects in the real world based on past experience, we cannot necessarily do so with completely abstract data that has a visual encoding where the height conveys meaning. For example, it is more difficult to judge bar heights in a 3D bar chart than in multiple horizontally aligned 2D bar charts." - this might have interesting philosophical implications, would serve as evidence supporting the empiricist view.

"In philosophyempiricism is a theory of knowledge that asserts that knowledge arises from evidence gathered via sense experience. Empiricism is one of several competing views that predominate in the study of human knowledge, known as epistemology. Empiricism emphasizes the role of experience and evidence, especially sensory perception, in the formation of ideas, over the notion of innate ideas ortradition [1] in contrast to, for example, rationalism which relies upon reason and can incorporate innate knowledge." (Wikipedia)

5th October, Polaris article:

Database visualization systems could be utilized to 'enhance'/clarify the effect and implications of the produce of DM.

Q:  What is the relation between polaris interface and tableau public? Does the latter utilise the the first? Or are they competitors?  
A: Tableau Software was recently honored by the computer science publication, Communications of the ACM (CACM). 50,000+ members of theACM (Association for Computing Machinery) just received the November issue featuring a research paper by Chris Stolte, Diane Tang and Pat Hanrahan. Titled "Polaris: A System for Query,
Analysis, and Visualization of Multidimensional Databases", it's about their invention Polaris, now called VizQL, the founding technology under Tableau Software. It also includes a foreword by Jim Gray, a pioneer, legend and brilliant mind in computer science. Jim Gray’s name may sound familiar; he’s the computer scientist who unfortunately went missing off the San Francisco Bay 2 years ago. Pat Hanrahan's recent blog entry describes how important Jim was to his and Chris's research.
             
Comments